体素匹配NORDIC：通过时间序列相似性进行非局部块形成，提高高分辨率BOLD fMRI的tSNR
Voxel-Matching NORDIC: Non-local patch formation by time-series similarity increases tSNR in high-resolution BOLD fMRI
- University Medical Center Utrecht（乌得勒支大学医学中心）
- Spinoza Center for Neuroimaging Amsterdam（阿姆斯特丹斯皮诺扎神经影像中心）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对亚毫米BOLD fMRI低信噪比问题，提出基于时间序列相似性收集非局部体素形成块的VM-NORDIC去噪算法，通过增强信号冗余促进低秩性，提升tSNR并减少空间平滑度损失。
中文摘要 AI 辅助
基于血氧水平依赖（BOLD）信号的亚毫米功能磁共振成像（fMRI）使得在亚毫米水平研究脑功能成为可能，揭示皮层分层和柱状结构等精细组织的见解。然而，其固有的低对比度噪声比（CNR）和信噪比（SNR）常常限制其可靠性和适用性。基于分布校正主成分分析的噪声降低（NORDIC PCA）是一种局部低秩去噪算法，以局部块方式降低BOLD fMRI中的热噪声水平。然而，局部块通常包含来自多种组织的信号混合，这会对块的低秩结构产生负面影响，从而限制算法的去噪能力。我们提出了一种替代的块形成方法，即通过收集相似的非局部体素，称为体素匹配（VM）NORDIC。在亚毫米分辨率BOLD fMRI数据上的结果表明，VM-NORDIC通过增强信号冗余有效促进块的低秩性，从而实现更高效的噪声衰减。此外，由于基于时间序列相似性的非局部体素选择，该方法几乎不影响空间平滑度。特别是，VM-NORDIC在时间信噪比（tSNR）和空间平滑度估计方面优于采用默认局部块的标准NORDIC（Standard-NORDIC）（tSNR比Standard-NORDIC大约9-90%，比原始数据大约23-250%；空间平滑度约为Standard-NORDIC诱导平滑度的20%）。这些改进对于提高亚毫米分辨率fMRI研究的有效性和精确性至关重要。
英文摘要
Submillimeter functional magnetic resonance imaging (fMRI) based on blood-oxygenation-level-dependent (BOLD) signal enables the study of brain function at the submillimeter level, uncovering insights into fine-scale organisations like cortical layers and columns. However, its inherently low contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) often limit its reliability and applicability. Noise Reduction with Distribution Corrected Principal Components Analysis (NORDIC PCA) is a locally low-rank denoising algorithm that reduces thermal noise levels in BOLD fMRI in a local patch manner. However, local patches often contain a mixture of signals from multiple tissues that negatively affects the low-rank structure of the patches, which limits the denoising capabilities of the algorithm. We propose an alternative approach for patch formation by gathering similar non-local voxels, dubbed voxel-matching (VM) NORDIC. The results on submillimeter-resolution BOLD fMRI data indicate that VM-NORDIC effectively promotes the low rank of the patches by boosting signal redundancy, allowing for more efficient noise attenuation. Moreover, the method barely affects spatial smoothness due to the non-local voxel selection based on time-series similarity. In particular, VM-NORDIC outperforms standard NORDIC with default local patching (Standard-NORDIC) in terms of temporal SNR (tSNR) (~9-90% larger than Standard-NORDIC; ~23-250% larger than the original) and spatial smoothness estimates (~20% of the smoothness induced by Standard-NORDIC). These improvements are fundamental to improving the validity and precision of fMRI studies at submillimeter resolutions.
